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Machine Learning Engineering

A practical, end-to-end guide to the engineering principles and best practices required to successfully build, deploy, and maintain machine learning systems in production.

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What it’s about

While many resources teach the theory and algorithms of machine learning, this book bridges the critical gap between building a model and deploying a robust, scalable, and maintainable ML system in the real world. It addresses the entire project lifecycle, from defining business goals and collecting data to feature engineering, model deployment, monitoring, and maintenance. Readers will learn to navigate the common pitfalls that cause most ML projects to fail, such as data leakage, distribution shift, and the lack of proper infrastructure. It's a comprehensive manual for data analysts becoming ML engineers, current ML engineers seeking more structure, and software architects who need to integrate ML models into production systems.

The through-line

Who it’s for
A data analyst, data scientist, or software engineer who knows the basics of machine learning but struggles to translate their models into real-world, production-grade applications that deliver business value. They want to move beyond notebooks and build robust, scalable, and maintainable ML systems.
The problem
Their machine learning projects get stuck in the prototype phase, take too long to deploy, or fail to perform as expected in production. They face engineering challenges like data quality issues, deployment complexity, and performance degradation over time. They feel frustrated and uncertain about how to bridge the gap between ML theory and practical engineering. They are overwhelmed by the complexity of building end-to-end systems and fear their models will fail silently, costing the business time and money.
The plan
  1. Project Scoping: Learn to prioritize ML projects, define clear goals, and understand why projects fail.
  2. Data Management: Master the principles of data collection, preparation, versioning, and avoiding common data problems like bias and leakage.
  3. Feature Engineering: Systematically create, select, and manage features to build a robust foundation for your models.
  4. Model Lifecycle Management: Follow a structured process for training, evaluating, deploying, serving, monitoring, and maintaining models in production.
  5. Engineering Best Practices: Adopt proven software and ML engineering practices for building reliable, scalable, and maintainable systems.
The payoff
The reader can confidently lead and execute machine learning projects from conception to production. · They build scalable, reliable, and maintainable ML systems that deliver consistent business value. · They become a highly sought-after machine learning engineer, capable of bridging the gap between data science and software engineering.

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